Spring Data JPA is a powerful framework that lets you interact with your database without writing a lot of boilerplate code.
00:00 - Intro
00:37 - Creating a New Spring Boot Project
03:13 - Creating an Employee Entity
08:03 - Creating a Repository Interface
09:40 - Configuring your Database
12:02 - Writing from your Application to the Database
14:50 - Creating a Data Source
18:03 - Declaring a Query Method
*Author: Dalia Abo Sheasha
You can find the GitHub repository containing the final project here: https://github.com/daliasheasha/SpringDataJPA
#spring #data #jpa #intellij #database #entity #java #programming
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
Business data is more bountiful than ever. Regardless of whether this data is gathered directly or bought from a third-party or syndicated source, it must be appropriately managed to bring organizations the most worth.
To achieve this goal, organizations are putting resources into data infrastructure and platforms, for example, data lakes and data warehouses. This investment is crucial to harnessing insights, yet it’s only essential for the solution.
Organizations are quickly embracing data-driven decision making processes. With insight-driven organizations growing multiple times quicker than their competitors, they don’t have a choice.
The gauntlet has adequately been tossed down. Either give admittance to significant data for your business, or join the developing memorial park of dinosaur organizations, incapable or reluctant to adapt to the cutting-edge digital economy
Self-service BI and analytics solutions can address this challenge by empowering business owners to access data straightforwardly and gain the insights they need. Nonetheless, just offering Self-service BI doesn’t ensure that an organization will become insights-rich and that key partners will be able to follow up on insights without contribution from technical team members.
The progress to genuinely insights-driven decisions requires a purposeful leadership effort, investment in the correct devices, and employee empowerment with the goal that leaders across capacities can counsel data independently prior to acting.
As such, organizations must take a stab at data democratization: opening up admittance to data and analytics among non-technical people without technical guards. In data democratization, the user experience must line up with the practices and needs of business owners to guarantee maximum adoption.
Data democratization means the process where one can utilize the data whenever to make decisions. In the company, everybody profits by having snappy admittance to data and the capacity to make decisions instantly.
Deploying data democratization requires data program to be self-aware; that is, with more prominent broad admittance to data, protocols should be set up to guarantee that users presented to certain data comprehend what it is they’re seeing — that nothing is misconstrued when deciphered and that overall data security itself is kept up, as more noteworthy availability to data may likewise effectively build risk to data integrity. These protections, while vital, are far exceeded by the perception of and data contribution from all edges of a company. With support empowered and encouraged across a company’s ecosystem,further knowledge becomes conceivable, driving advancement and better performance.
#big data #data management #latest news #4 key tips to get started with data democratization #data democratization #key tips to get started with data democratization
Refactoring is the process of modifying a software system without changing its desirable behavior. It was necessary to have an application integrated with the relational database using the Spring JDBC Template in the first parts. The Spring JDBC Template is a powerful tool that facilitates productivity. However, there is a way to simplify the code even further with Spring Data JPA. The purpose of this post is to refactor the project to use Spring Data JPA.
Spring Data JPA, part of the larger Spring Data family, makes it easy to implement JPA-based repositories easily. This module deals with enhanced support for JPA-based data access layers. It makes it easier to build Spring-powered applications that use data access technologies.
A safe code refactoring requires the use of tests to ensure that the compartment is not changed. The use of tests, fortunately, is adopted as a minimum standard, including several methodologies such as TDD that preach the creation of tests at the beginning of the development process.
#java #tutorial #spring #spring data #java tutorial #spring tutorial #spring data jpa
We live in a world where billions of data points are generated every single day from different sources, such as banks, telecommunication companies, industries, tourism, the agriculture sector, educational institutions (primary, secondary, colleges, and universities), and mobile devices. Any organization can start using their data to make data-driven decision-making that is effective and supportive of their mission and vision.
Regardless of the size of the business you’re running, you need valuable data to provide you with business insights. The insights help you to know your target audience and their preferences, and as a result, your business will be able to anticipate their needs. You can use insights from big data to outperform your competition by capturing and innovating through big data.
Companies like Google and Alibaba are using it to discover flaws in their services and products, suppliers and buyers, and consumer intent and preferences so they can create newer, better ones.
#data #data-science #big-data #big-data-analytics #analyzing-big-data #artificial-intelligence #machine-learning #data-analytics